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Children’s Physical Activity And Mental Health

Principal Supervisor: Professor Eirini Flouri (IOE)

Co- Supervisor: Professor Steven Cummins (LSHTM)

The aim of this studentship is to explore the nature of the association between children’s mental health, measured broadly as internalising (i.e., peer and emotional problems) and externalising (i.e., conduct problems and hyperactivity/inattention problems), and objectively-measured (via accelerometer) physical activity and sedentary behaviour. The project will use data from the UK's Millennium Cohort Study (MCS; www.cls.ioe.ac.uk/mcs).

It is expected that the association may be explained by the impact of physical activity on other aspects of child functioning, but that the mechanisms may vary by domain of mental health. For example, physical activity may mitigate internalising symptoms via improved self-esteem, and externalising problems via improved cognition. It is also possible that the association may be moderated by child, family, and contextual (such as neighbourhood) factors. For example, physical activity may be particularly beneficial for specific groups of children or only when it is moderate rather than light. This studentship will attempt to explore and explain the association between children’s physical activity and their mental health (objective #1), and specify the groups and contexts in which it may be stronger (objective #2).

The student will meet these objectives using longitudinal data from MCS, a prospective study of children born in the UK in 2000-2002. The original cohort comprised 18,818 children. Since then, data have been collected when the children were aged 3, 5, 7 and 11 years. At age 7, 14,043 children (13,681 singletons) were interviewed and invited to participate in the accelerometry study. Accelerometers were returned by 9,772 singletons. In MCS, data measuring children’s mental health (with the Strengths and Difficulties Questionnaire) are available at ages 3, 5 and 7. In 2014, they will be available for age 11, too.

Candidate requirements

Graduates with a good first degree and/or Masters degree in psychology, social or medical statistics, quantitative human geography or other relevant social science with an interest and aptitude for quantitative methods and statistical modelling are encouraged to apply. The project will require advanced statistical analysis skills (e.g., R for modelling the accelerometer-based data, Mplus for the use of structural equation models, MlwiN if the context-based moderator variables describe low-geography characteristics, e.g., area green space, and observed data are used). Specific training on statistical modelling will form part of the MPhil/PhD training.